Search Results (41)

To investigate the influence of external ambient temperature on the transmission characteristics of laser propagation in an internal channel, a simulation model of laser transmission within a closed channel is established in this study. The model comprehensively considers factors including gas density, refractive index distribution, and thermal deformation of optical components. Based on optical transmission theory, the model is used to calculate the beam drift characteristics and the variation in the Strehl ratio at different temperatures. The results indicate that ambient temperature has a significant impact on beam stability and quality. At low temperature (−30 °C), speckle structures appear in the laser spot, with minor drift along the X direction but obvious negative drift along the Y direction, mainly caused by the sinking of cold air driven by gravity and the refractive index gradient. The beam drift decreases initially with increasing temperature, reaches its minimum at around 10 °C, and then increases gradually as the temperature continues to rise. The Strehl ratio initially increases during the early stage of temperature rise, but diminishes in the high-temperature range due to intensified gas disturbances, enhanced thermal lensing effects, and aggravated mirror surface deformation. Full article

Additively manufactured (AM) and wrought 316L stainless steels were irradiated with 2 MeV protons at 360 °C. Depth-resolved void swelling was quantified using cross-sectional transmission electron microscopy, with a safe-zone analysis applied to exclude near-surface and proton-range artifacts. The AM 316L exhibits significantly lower swelling than the wrought alloy. Swelling in the AM material is characterized by larger voids but a much lower void number density, whereas the wrought alloy develops smaller voids at a substantially higher density. The two alloys also display distinct dependences on local damage: in AM 316L, void size increases with local dpa while the void density remains nearly constant, whereas in wrought 316L, the void size is approximately constant and the void density increases with local dpa. These trends indicate that AM 316L has already entered a void growth-dominated regime, while wrought 316L remains in a void-nucleation-dominated regime. The reduced swelling in the AM alloy is attributed to more effective defect-recombination sinks and/or reduced vacancy mobility associated with the AM microstructure. These findings provide important insight for the evaluation and optimization of AM 316L alloys for nuclear industry applications. Full article

Vibration is a critical issue in aerospace structures, where lightweight design, high flexibility, and complex operational environments often lead to pronounced nonlinear dynamic responses. Excessive vibrations induced by harmonic excitations, aerodynamic loads, or onboard equipment can significantly degrade structural integrity, control accuracy, and service life. Consequently, advanced passive vibration suppression techniques with strong robustness and broadband effectiveness are of great importance in aerospace engineering applications. The bifurcation boundary and vibration suppression performance of Piezoelectric–Monostable Nonlinear Energy Sink (PMNES) are crucial for evaluating its effectiveness on the main structure. To simplify the analysis of flexible aerospace structures, a reduced-order model is derived by modal truncation in the low-frequency range, which is then treated as a two-degree-of-freedom main structure. To focus on the underlying nonlinear dynamic mechanisms, an equivalent two-degree-of-freedom lumped-parameter system is adopted as a generic representation of the dominant low-frequency dynamics of flexible aerospace structures. In this work, the electromechanical coupling control equations of the system of a two-degree-of-freedom main structure coupled with PNES are derived through the application of Newton’s second law and Kirchhoff’s voltage law. The methods of complexification-averaging (CX-A) and Runge–Kutta (RK) are employed to assess the vibration suppression performance and stability characteristics of the system under harmonic excitation. The approximate solution is validated through numerical solutions. The approximate solutions of the system are employed to derive the Saddle Node (SN) bifurcation and codimension-two cusp bifurcation points, while the enhanced algorithm is employed to ascertain the most unfavorable amplitude at each external excitation circular frequency and to determine whether the mark represents a Hopf Bifurcation (HB) point. The generalized transmissibility is utilized to assess the efficacy of vibration suppression. The various vibration suppression efficiency regions are created by superimposing the vibration suppression efficiency maps and bifurcation maps. The influence of PNES parameters on the vibration suppression region is investigated. The results indicate that this method can effectively evaluate the bifurcation boundary and vibration suppression performance of PMNES. Full article

Monitoring soil CO₂ is essential for accurately quantifying the sources and sinks of atmospheric greenhouse gases and for providing carbon emission reduction strategies. However, the limited portability and high cost of conventional soil CO₂ monitoring equipment have severely restricted large-scale and long-term field observations. To address these constraints, this study has successfully designed and fabricated a portable and low-cost soil respiration system (SRS) based on non-dispersive infrared (NDIR) sensor technology and Long-range radio (LoRa) wireless communication. The SRS enables multi-point synchronous measurements and remote data transmission. Its reliability was rigorously evaluated through both simulated and field comparative experiments against the LI-8100A. The results demonstrated a high level of agreement between the measurements of the SRS and the LI-8100A, with the coefficients of determination (R²) of 0.996 and 0.997, respectively, for the simulation and field experiments, with the corresponding root mean square error (RMSE) of 0.090 and 0.089 μmol·m⁻²·s⁻¹. The Bland–Altman analysis further confirmed the consistency between the two systems, with over 95% of the data points falling within the acceptable limits of agreement. These findings indicate that the self-developed SRS substantially reduces costs while maintaining reliable measurement accuracy. With its wireless transmission and multi-point deployment capabilities, the SRS offered an efficient and practical solution for addressing the challenges of monitoring spatial heterogeneity of soil respiration, demonstrating considerable potential for broader application in CO₂ flux monitoring research. Full article

Pine wilt disease (PWD) is one of the fastest-spreading invasive forest pathogens worldwide, causing rapid mortality of infected trees and posing a severe threat to global forest ecosystem security and carbon sink capacity. However, the spatial dynamics and diffusion characteristics of PWD at the stand scale remain poorly understood. In this study, we selected a typical epidemic area in Qingyuan County, Liaoning Province, China, as the study site. By integrating 23 phases of unmanned aerial vehicle (UAV) multispectral imagery, airborne LiDAR data, and field survey observations, we reconstructed the spatiotemporal diffusion process of PWD from 2023 to 2025 and developed a stand-scale, tree-level mortality risk prediction model. Our results show that 50% of transmission events occurred within 17.2 m, and the spatial autocorrelation range was approximately 28 m. The peak of the lethal latency period occurred 17 days after infection, with 40% of mortality events occurring within 11–22 days and 50% of infected trees dying within 40 days. The latency period was significantly shorter in spring and summer than in winter ($p<0.01$). Among tree-level mortality risk prediction approaches, the random forest model performed best, improving overall accuracy by more than 15% compared with other methods and correctly identifying 98.6% of high-risk individuals. The distance to the nearest infected or dead tree was identified as the dominant predictor, followed by tree height and vegetation parameters reflecting host physiological status. This study reveals the spatial diffusion characteristics of PWD at the stand scale and proposes a tree-level risk prediction framework, providing a theoretical foundation and technical support for dynamic monitoring, early warning, and precision management of PWD. Full article

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. Full article

In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided. Full article

Eutrophication creates multiple environmental problems, threatening the ecological security and sustainability of estuarine and coastal ecosystems worldwide. Key nutrients of concern are nitrogen (N) and phosphorus (P), which are the main controls in eutrophication. Considering that sediments are inseparable sinks of N and P, concern has grown regarding the forms in which N and P occur in the surface sediments of estuaries and coastal areas. Nonetheless, studies on the natural N-bearing or P-bearing nanoparticles in estuarine and coastal sediments have rarely been reported. Herein, the surface sediments (0–5 cm) of the Pearl River Estuary in China were collected and subjected to analysis. Using high-resolution transmission electron microscopy (HR-TEM) analysis, numerous natural N-bearing and P-bearing nanoparticles were observed. The results revealed that there are some differences in the occurrence forms of N and P in nanoparticles, suggesting that N and P could be adsorbed by nanoparticles of minerals such as hematite, goethite, muscovite, anorthite and quartz in estuarine and coastal environments, and further form N-bearing and P-bearing nanoparticles. These nanoparticles contained small amounts of N (1.52–3.73 wt%) and P (0.22–1.12 wt%), and were mainly single crystal or polycrystalline in form, with sizes ranging from 10 nm × 50 nm to 250 nm × 400 nm. In addition, P was shown to exist in the form of Ca and Fe phosphate nanoparticles in the estuarine sediments. The Ca and Fe phosphate nanoparticles had higher phosphorus content (5.02–9.97 wt%), mainly amorphous, with sizes ranging from 50 nm × 120 nm to 250 nm × 400 nm. Moreover, N-bearing and P-bearing nanoparticles could influence the migration, precipitation and release processes of N and P, and play a certain role in the N-cycling and P-cycling of estuarine and coastal ecosystems. Furthermore, we explored the role of N-bearing and P-bearing nanoparticles in the N-cycling and P-cycling in estuarine and coastal ecosystems. Thus, this study could provide new ideas for water environment management and other related research fields. Full article

Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels. Full article

As sessile organisms, plants face a wide range of abiotic stresses, with salinity being a significant condition affecting their growth, development, and productivity, particularly in arid and semi-arid regions. This study focused on understanding how salinity impacts Jatropha curcas, an important oilseed plant for the production of biodiesel. By examining the anatomy and ultrastructure of stomata and chloroplasts, we investigated the effects of prolonged salinity stress on J. curcas. This stress led to changes in the stomatal density, stomatal index, and ostiole aperture, which can cause an imbalance of water conductivity in the xylem. Through transmission electron microscopy, we explored the subcellular organization of J. curcas chloroplasts and their contribution to plant photosynthetic efficiency, providing insights into their role in this process. Notably, increases in salinity resulted in a significant increase in starch granule accumulation, leading to impaired granal and stromal grana lamellae, destroying this ultrastructure. Our findings indicate that the anatomy and ultrastructure of chloroplasts play a crucial role in influencing photosynthetic efficiency. Moreover, impaired hydraulic conductivity due to salinity and a lesser osmotic potential in vessels may cause a reduced source-to-sink relationship, which increases starch accumulation in the chloroplast and influences the ultrastructure of the chloroplast. This study offers a new perspective on the structure and function of chloroplasts in J. curcas, presenting innovative opportunities to develop strategies that enhance the production of biofuel in areas with high soil salinity. Full article

Ion irradiation is a promising tool to emulate neutron-irradiation effects on reactor pressure vessel (RPV) steels, especially in the situation of limited availability of suitable neutron-irradiated material. This approach requires the consideration of ion-neutron transferability issues, which are addressed in the present study by comparing the effect of ions with neutron-irradiation effects reported for the same materials. The first part of the study covers a comprehensive characterization, based on dedicated electron microscopy techniques, of the selected unirradiated RPV materials, namely a base metal and a weld. The results obtained for the grain size, dislocation density, and precipitates are put in context in terms of hardening contributions and sink strength. The second part is focused on the depth-dependent characterization of the dislocation loops formed in ion-irradiated samples. This work is based on scanning transmission electron microscopy applied to cross-sectional samples prepared by the focused ion beam technique. A band-like arrangement of loops is observed in the depth range close to the peak of injected interstitials. Two levels of displacement damage, 0.1 and 1 dpa (displacements per atom), as well as post-irradiation annealed conditions, are included for both RPV materials. Compared with neutron irradiation, ion irradiation creates a similar average size but a higher number density of loops presumably due to the higher dose rate during ion irradiation. Full article

In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data. Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path where these points will stay fixed during the whole network lifetime, we propose an adaptive path construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are: the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value for each sensor node generated based on the previous three parameters and, thus, each RP is updated to its new location accordingly. Simulation results have shown that the proposed approach extends the network lifetime compared with another existing approach that uses fixed RPs. For example, in terms of using the first dead node as a metric for the network lifetime, when the number of deployed sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been achieved compared with another related approach that uses fixed RPs. Full article

Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people’s mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works. Full article

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay. Full article